In this work, effects of using different evaporative cooling pads (ECPs) on the energetic and exergetic efficiency of a direct evaporative air cooler (DEAC) have been theoretically and experimentally investigated. Three types of ECPs were used, i.e., honeycomb cellulose cooler pad (HCCP), shading-cloth cooler pad (SCCP), and aspen wood wool cooler pad (AWWCP). For SCCP and AWWCP, a 3-cm pad thickness was used, while for the HCCP, three different values of pad thickness were used, i.e., 3, 5, and 7 cm. Tests were carried out using air velocities of 8, 14, and 19 m/s, measured at the DEAC outlet. Engineering equation solver (EES) used for performing the required calculations of the various parameters affecting the thermal performance of the D

Plant extracts occupied a big place in diseases treatment and preserving human health because, they contain many active substances that can be exploited in the field of pharmaceutical manufacturing from natural materials. Therefore, this study was conducted to evaluate the effect of different concentrations of plant extracts for each of Nigella sativa, Alliumsativum and Allium cepa against the fungal growth of Candida albicans that cause many skin diseases and infections to humans as well as Trichophyton mentagrophytes, which affects the hair, skin and nails. These two fungi have been isolated and diagnosed from people who have skin infection. Both fungal isolates were treated with extracts of Nigella sativa, Alliumsativum and Allium cepa

197 vaginal swabs were collected from women of different ages. (60) Isolates of Candida albicans (30.4%) were obtained, and the other species of Candida represent (18.27%). Bacterial infections showed (41.11%), and infection with Trichomonas vaginalis was (2.03%). Ten isolates of C. albicans were chosen randomly for farther study which include two virulence factors tendency of adhesion wich showed a percentage of (52%) to(32%) , and the ability to produce phospholipaze enzyme and it’s activity which showed (50%) of the isolates have the ability to produce the enzyme in different degrees .

    This article aims to explore the importance of estimating the a semiparametric regression function ,where we suggest a new estimator beside the other combined estimators and then we make a comparison among them by using simulation technique . Through the simulation results we find  that the suggest estimator is the best with the first and second models ,wherealse for the third model we find Burman and Chaudhuri (B&C) is best.

The aim of this study was investigating the correlation between elevation of Prolactin levels and the increase of the concentrations of total sialic acids. The study was performed on 149 women consisted of 93 infertile hyperprolactinimic women (patients), age ranged16-38 years old, and 56 normoprolactinemic women as a control group, 18-37 years old. Serum prolactin (PRL) and gonadotroph hormones (Follicle stimulating hormone FSH and Luteinizing hormone LH) were measured using enzymatic immunoassay (EIA) method, resorcinol method for serum total sialic acids (SIA). Patients were divided into four groups, each group represented the level of prolactin of infertile women as follow: G1= (21-30), G2= (31-40), G3= (41-50), and G4= (51-60) ng/mL. S

Background: Osteoporosis is denoted by low bone mass and microarchitectural breakdown of bone tissue, directing to increased fracture risk and bone fragility. Fractures may lead to a decreased quality of life and increased medical costs. Thus, osteoporosis is widely considered a significant health concern.

Objective. This study aimed to compare quantitative computed tomography (QCT) and dual-energy X-Ray absorptiometry (DXA) to detect osteoporosis in postmenopausal women.

Subjects and Methods. We measured spinal volumetric bone mineral density (BMD) with QCT and areal spinal and hip BMD with DXA in 164 postmenopausal women. We calculated the osteo

Abstract Software-Defined Networking (commonly referred to as SDN) is a newer paradigm that develops the concept of a software-driven network by separating data and control planes. It can handle the traditional network problems. However, this excellent architecture is subjected to various security threats. One of these issues is the distributed denial of service (DDoS) attack, which is difficult to contain in this kind of software-based network. Several security solutions have been proposed recently to secure SDN against DDoS attacks. This paper aims to analyze and discuss machine learning-based systems for SDN security networks from DDoS attack. The results have indicated that the algorithms for machine learning can be used to detect DDoS